AI in Finance Summit New York schedule

•    Enhance risk detection with LLMs that learn from historical investigations and analyst notes.
•    Empower investigation teams with AI copilots for instant insights, narratives, and recommended actions.
•    Automate AML/KYC processes using explainable, auditable AI for transparent compliance.
•    Strengthen defenses by leveraging AI to proactively identify emerging threats and reduce noise

 

 

Can we open the black box of large language models and make AI in finance truly transparent? As financial institutions adopt LLMs for tasks like trading, compliance, and advisory, the need for transparency is more critical than ever. Understanding how these models internally reason about financial topics is key to ensuring trust and regulatory alignment. This talk covers how mechanistic interpretability can reveal internal patterns in LLMs related and use it for applications such as trading, sentiment analysis and hallucination reduction.

Every day brings promises of a new, transformational AI use case. Meanwhile, your organization is racing to move from proof-of-concepts to secure, production-grade AI apps, tools and systems. Achieving this requires a strategic, enterprise-wide approach to operationalizing AI. In this session, Purnima Padmanabhan introduces an actionable framework designed to address the real-world challenges of operationalizing AI in highly regulated industries like Finance. Topics include:
•    Agentic Runtime Considerations: Establishing guardrails, observability and standardization for autonomous AI agents operating in secure, regulated environments.
•    Data & AI Sovereignty: Balancing regulatory compliance and local control with the flexibility of an open ecosystem of models, frameworks and data sources.
•    Robust, Centralized AI Governance: Defining governance for agents such as coding assistants, and enforcing controls on agent access to sensitive enterprise tools and data

•    What are the most effective strategies to scale AI initiatives while keeping costs under control?
•    How can organizations balance the transformative potential of generative AI with the risks it introduces?
•    In what ways AI Agents are reshaping business operations, and what challenges come with their adoption?
•    How do you measure and ensure tangible business value from AI investments across the enterprise?

Panelists:
Sreekar Bhaviripudi, Head of Machine Learning, MORGAN STANLEY
Krishan, Senior Vice President Model Risk Management, CITI
Raj Gunukula, Group Technical Program Manager, COINBASE
Alp Basol, Head of Artificial Intelligence, COBANK
Sai Zeng, Head of AI, Executive Director, Investment Banking & Global Capital Markets Technology, MORGAN STANLEY

 

•    How multi-agent systems are driving real-time decision-making in trading, risk, and compliance
•    The role of AI orchestration in creating autonomous financial ecosystems
•    Balancing innovation with human oversight and regulatory trust

Panelists:
Michael Mocanu, Sr. Director, Data Science & AI, LIBERTY MUTUAL INSURANCE
Bijit Ghosh, Managing Director - AI/ML/Data, WELLS FARGO
Brij Mohan, Vice President-Software Development, LPL FINANCIAL
Jake Katz, Head of Analytics Research, LONDON STOCK EXCHANGE GROUP
Lily Li, Head of AI Adoption and Solutions, FRANKLIN TEMPLETON

•    Using AI for secure, real-time global transactions
•    Overcoming fraud and data challenges in AI-powered payments 
•    The role of AI in CBDCs and next-gen payment rails 

 

Live demonstrations to explore how AI agents can be embedded into document/spreadsheet workflows in finance where intelligence becomes part of daily work (e.g., FP&A, reporting, modelling). Experience real-time interactions with AI agents across documents, data, and presentations, showing how a single agentic approach can be applied consistently across recurring, common themes
Build and customize simple AI agents during the session, giving participants first-hand experience of rapid agent development with practical guardrails, triggers, and approvals - without conventional engineering efforts.

• How can we use real-time data streams to make better LLMs? (ex: fine tuning)
• How can we use real-time data for better AI inferencing? (ex: observability & evaluation)
• How can we use real-time data for agentic systems (RAG and MCP architectures)?
• Documented here

•    Early signals of quantum computing integration with AI for portfolio optimization and fraud detection
•    Potential breakthroughs and realistic adoption timelines for finance
•    Managing hype vs. reality: what leaders should invest in today

Discover groundbreaking AI technologies aiming to transform finance.

•    Agentic AI driving smarter trading decisions.
•    Multi-agent systems for better forecasting and risk.
•    Quantum computing reshaping compliance.
•    Strategic impact for financial institutions.

•    Thriving in uncertainty, AI built to perform under market volatility and rapid change.
•    Trust by design, resilience rooted in transparency, governance, and ethical AI.
•    Future-ready finance, adaptable AI that meets new risks, regulations, and customer demands

•    How does poor data quality directly undermine AI outcomes in finance?
•    What governance practices truly build trust in AI-driven decisions?
•    How can institutions balance innovation with strict data controls?
•    What lessons from early adopters show the ROI of strong data governance?

Panelists:
Tyler Frieling, Director, Emerging Technologies, BLACKROCK
Anupama Garani, AI & Machine Learning, PIMCO
Schitiz Saxena, Director - Chief Data Office, TD
Julia Cherashore, Senior Fellow, DATA FOUNDATION
Nishit Dhilen Mehta, Vice President, Data Analytics, JPMORGAN CHASE

•    Failures First – What early missteps revealed about AI’s real limits in finance.
•    Scaling Wins – How pioneers turned pilots into enterprise-wide impact.
•    Trust Factor – Building governance and transparency from the start.
•    Next Moves – What early adopters see as the boldest opportunities ahead

•    By 2030, what will distinguish financial institutions that successfully harness AI from those that fall behind?
•    Which areas of the financial sector are most ripe for AI-driven transformation in the next five years?
•    How can we balance automation with transparency, fairness, and human oversight as AI becomes central to financial decision-making?
•    How will AI reshape the roles, skills, and culture of professionals in the finance industry by 2030?

Panelists:
Arjun Wadwalkar, Senior Manager of Product Management, GLOBAL PAYMENTS
Jake Katz, Head of Analytics Research, LONDON STOCK EXCHANGE GROUP
Matt Goldwasse, Head of AI Data Science, T. ROWE PRICE 
Aakanksha Jadhav, Director Product Development, MASTERCARD
Jaydip Mukhopadhyay, Vice President, Data Science and Model Risk, AMERICAN EXPRESS
 
 

•    From Pilots to Scalable Impact: How financial institutions can evolve AI maturity to unlock measurable business outcomes.
•    The Growth Catalyst: Why AI maturity is the key to driving efficiency, innovation, and revenue generation in financial services.
•    Building Trust at Scale: How to align governance, compliance, and customer trust as AI capabilities mature.

 

 

•    How can financial institutions strike the right balance between rapid AI innovation and strict compliance requirements?
•    What are the most common security risks when deploying AI in finance, and how can organizations mitigate them?
•    How can firms ensure that AI tools are not only compliant at launch but remain compliant as regulations evolve?
•    What does it take to scale AI solutions across an enterprise without compromising governance or customer trust?
•    Looking ahead, what capabilities will define the next generation of AI tools that are truly enterprise-ready for finance

Panelists: 
Dhivya Nagasubramanian, Vice President, AI Transformation & Innovation, U.S. BANK 
Leah Price, Vice President, Tinman AI Platform, BETTER 
Ellis Wong, Chief Information Security Officer, JST CAPITAL 
Harry Mendell, Data Architect Technology Group, FEDERAL RESERVE BANK OF NEW YORK 
Izge Cengiz Ercan, Director of Strategic Innovation, VALLEY BANK 

Assess the shift from discrete AI agents to embedded, ambient intelligence within core finance and customer-facing systems, and present implications for scale, governance, and user adoption
Understand why human-in-the-loop design and event-driven triggers are critical for actionable, auditable, and trusted AI in regulated environments.
Highlight how new development paradigms enable business teams to design and deploy internal AI agents faster, unlocking speedy domain-led innovation while staying aligned with risk and compliance requirements.

Discover groundbreaking AI technologies aiming to transform finance

•    How can financial institutions ensure the quality, integrity, and governance of customer data to drive reliable AI outcomes?
•    In what ways does trusted customer data accelerate the development of innovative, personalized financial products and services?
•    What are the biggest challenges in balancing customer privacy with the need for data to fuel AI innovation?
•    How can organizations build and maintain customer trust while scaling AI solutions that rely heavily on sensitive data?

Panelists: 
Reema Gill, Data/AI Governance Specialist, WEALTHSIMPLE 
Shruti Iyer, Senior Data Scientist, CAPITAL ONE 
Shaurya Tripathi, Senior Data Scientist, MUNICH RE 
Santosh Gaikwad, Executive Director - Artificial Intelligence Platform Engineering Lead, JPMORGAN CHASE 

•    Beyond automation: how AI agents move from task execution to strategic decision-making
•    Efficiency unlocked: streamlining operations and reducing costs across the financial ecosystem
•    Revenue engine: transforming AI agents into drivers of growth, new products, and customer value
•    The future of finance: AI agents reshaping roles, talent, and business models

•    Domain Accuracy: Assess how LLMs perform on finance-specific reasoning tasks  
•    Safety & Compliance: Measure domain-specific safety, ethics, and regulatory alignment 
•    Cost vs Capability Trade-offs: Compare performance, inference cost, latency, and scalability in live financial use
•    Agentic & Tool-Enabled Behavior: Go beyond single-response metrics: evaluate reasoning chains, tool use, memory, and multi-step workflows

In a field dominated by models and metrics, this session reframes AI explainability by focusing first on the people who rely on these systems. Drawing on real examples from banking and financial services, it introduces a practical, human-centered framework that helps AI leaders and business stakeholders jointly identify where improved prediction or agentic systems can truly transform the organization. Participants will learn how to map critical decisions and processes, score AI use cases by business value and explainability requirements, and design explanations that different audiences can readily understand and act on.

Attendees will leave with concrete tools to run more effective AI-use-case workshops, avoid unfocused ‘science projects,’ and build AI systems that are not only high-performing but responsible, transparent, and broadly trusted across the institution.




 

Trust, transparency, and explainability as the foundation for AI adoption in financial services.
How evolving regulations are shaping responsible AI design and governance.
Turning ethical AI principles into practical, scalable implementation.
Balancing innovation with risk management and long-term compliance.